SoroushVahidi/scidocs huggingface dataset:
Accept or appropriate ? A design-oriented critique on technology acceptance models
(2009)

Technology acceptance models are tools for predicting users’ reception of technology by measuring how they rate statements on a questionnaire scale. It has been claimed that these tools help to assess the social acceptance of a final IT product when its development is still underway. However, their use is not without problems. This paper highlights some of the underlying shortcomings that arise particularly from a simplistic conception of “acceptance” that does not recognize the possibility that users can invent new uses for (i.e., appropriate) technology in many situations. This lack of recognition can easily lead one to assume that users are passive absorbers of technological products, so that every user would adopt the same usages irrespective of the context of use, the differences in work tasks, or the characteristics of interpersonal cooperation. In light of recent research on appropriation, technology use must actually be understood in a more heterogeneous way, as a process through which different users find the product useful in different ways. This paper maintains that if, in fact, a single technology can be used for multiple purposes, then subscribing to the thinking arising from technology acceptance model research may actually lead one into suboptimal design solutions and thus also compromise user acceptance. Therefore, this paper also presents some starting points for designing specifically for easier technology appropriation.
Privacy protection based access control scheme in cloud-based services
(2017)

With the rapid development of computer technology, cloud-based services have become a hot topic. They not only provide users with convenience, but also bring many security issues, such as data sharing and privacy issue. In this paper, we present an access control system with privilege separation based on privacy protection (PS-ACS). In the PS-ACS scheme, we divide users into private domain (PRD) and public domain (PUD) logically. In PRD, to achieve read access permission and write access permission, we adopt the Key-Aggregate Encryption (KAE) and the Improved Attribute-based Signature (IABS) respectively. In PUD, we construct a new multi-authority ciphertext policy attribute-based encryption (CP-ABE) scheme with efficient decryption to avoid the issues of single point of failure and complicated key distribution, and design an efficient attribute revocation method for it. The analysis and simulation result show that our scheme is feasible and superior to protect users' privacy in cloud-based services.
Liquid Leakage Thin Film-Tape Sensor
(2012)

A liquid leakage sensor on adhesive tape with alarming function has been developed. The film sensor tape consists of three layers: a base film layer, a substrate film layer, and a protective film layer. Three conductive lines and one resistive line are printed on the substrate film layer with a nano-sized silver conductive ink by the electronic gravure printing method. The location of liquid leakage has been found to be monitored accurately by applying the pulse voltage to the conductive and resistance lines in a periodic polarity switching mode. The leakage tests for various electrolyte liquids have been performed and the accuracy of leakage error position has been achieved to be about 0.25% on 200 meter long tape length.
The study on feature selection in customer churn prediction modeling
(2009)

When the customer churn prediction model is built, a large number of features bring heavy burdens to the model and even decrease the accuracy. This paper is aimed to review the feature selection, to compare the algorithms from different fields and to design a framework of feature selection for customer churn prediction. Based on the framework, the author experiment on the structured module with some telecom operator's marketing data to verify the efficiency of the feature selection framework.
Millimeter-wave large-scale phased-arrays for 5G systems
(2015)

This talk will present our latest work on silicon RFICs for phased-array applications with emphasis on very large chips with built-in-self-test capabilities for 5G systems. SiGe is shown to be ideal for mm-wave applications due to its high temperature performance (automotive radars, base-stations, defense systems, etc.) and lower power consumption. These chips drastically reduce the cost of microwave and millimeter-wave phased arrays by combining many elements on the same chip, together with digital control and some cases, high-efficiency antennas. The phased-array chips also result in an easier packaging scheme using either a multi-layer PCB or wafer-level packages. We believe that this family of chips will be essential for millimeter-wave 5G communication systems.
SLIQ: A Fast Scalable Classifier for Data Mining
(1996)

Classi cation is an important problem in the emerging eld of data mining. Although classi cation has been studied extensively in the past, most of the classi cation algorithms are designed only for memory-resident data, thus limiting their suitability for data mining large data sets. This paper discusses issues in building a scalable classier and presents the design of SLIQ, a new classi er. SLIQ is a decision tree classi er that can handle both numeric and categorical attributes. It uses a novel pre-sorting technique in the tree-growth phase. This sorting procedure is integrated with a breadthrst tree growing strategy to enable classi cation of disk-resident datasets. SLIQ also uses a new tree-pruning algorithm that is inexpensive, and results in compact and accurate trees. The combination of these techniques enables SLIQ to scale for large data sets and classify data sets irrespective of the number of classes, attributes, and examples (records), thus making it an attractive tool for data mining.
Sampling-Based Estimation of the Number of Distinct Values of an Attribute
(1995)

We provide several new sampling-based estimators of the number of distinct values of an attribute in a relation. We compare these new estimators to estimators from the database and statistical literature empirically, using a large number of attribute-value distributions drawn from a variety of real-world databases. This appears to be the first extensive comparison of distinct-value estimators in either the database or statistical literature, and is certainly the first to use highlyskewed data of the sort frequently encountered in database applications. Our experiments indicate that a new “hybrid” estimator yields the highest precision on average for a given sampling fraction. This estimator explicitly takes into account the degree of skew in the data and combines a new “smoothed jackknife” estimator with an estimator due to Shlosser. We investigate how the hybrid estimator behaves as we scale up the size of the database.
SPRINT: A Scalable Parallel Classifier for Data Mining
(1996)

Classification is an important data mining problem. Although classification is a wellstudied problem, most of the current classification algorithms require that all or a portion of the the entire dataset remain permanently in memory. This limits their suitability for mining over large databases. We present a new decision-tree-based classification algorithm, called SPRINT that removes all of the memory restrictions, and is fast and scalable. The algorithm has also been designed to be easily parallelized, allowing many processors to work together to build a single consistent model. This parallelization, also presented here, exhibits excellent scalability as well. The combination of these characteristics makes the proposed algorithm an ideal tool for data mining.
Inferring Decision Trees Using the Minimum Description Length Principle
(1989)

This paper concerns methods for inferring decision trees from examples for classification problems. The reader who is unfamiliar with this problem may wish to consult J. R. Quinlan’s paper (1986), or the excellent monograph by Breiman et al. (1984), although this paper will be self-contained. This work is inspired by Rissanen’s work on the Minimum description length principle (or MDLP for short) and on his related notion of the stochastic complexity of a string Rissanen, 1986b. The reader may also want to refer to related work by Boulton and Wallace (1968, 1973a, 1973b), Georgeff and Wallace (1984), and Hart (1987). Roughly speaking, the minimum description length principle states that the best “theory” to infer from a set of data is the one which minimizes the sum of
A low dropout, CMOS regulator with high PSR over wideband frequencies
(2005)

Modern system-on-chip (SoC) environments are swamped in high frequency noise that is generated by RF and digital circuits and propagated onto supply rails through capacitive coupling. In these systems, linear regulators are used to shield noise-sensitive analog blocks from high frequency fluctuations in the power supply. The paper presents a low dropout regulator that achieves power supply rejection (PSR) better than -40 dB over the entire frequency spectrum. The system has an output voltage of 1.0 V and a maximum current capability of 10 mA. It consists of operational amplifiers (op amps), a bandgap reference, a clock generator, and a charge pump and has been designed and simulated using BSIM3 models of a 0.5 /spl mu/m CMOS process obtained from MOSIS.
Optical Fiber Sensors for Aircraft Structural Health Monitoring
(2015)

Aircraft structures require periodic and scheduled inspection and maintenance operations due to their special operating conditions and the principles of design employed to develop them. Therefore, structural health monitoring has a great potential to reduce the costs related to these operations. Optical fiber sensors applied to the monitoring of aircraft structures provide some advantages over traditional sensors. Several practical applications for structures and engines we have been working on are reported in this article. Fiber Bragg gratings have been analyzed in detail, because they have proved to constitute the most promising technology in this field, and two different alternatives for strain measurements are also described. With regard to engine condition evaluation, we present some results obtained with a reflected intensity-modulated optical fiber sensor for tip clearance and tip timing measurements in a turbine assembled in a wind tunnel.
3DMatch: Learning the Matching of Local 3D Geometry in Range Scans
(2016)

Establishing correspondences between 3D geometries is essential to a large variety of graphics and vision applications, including 3D reconstruction, localization, and shape matching. Despite significant progress, geometric matching on real-world 3D data is still a challenging task due to the noisy, low-resolution, and incomplete nature of scanning data. These difficulties limit the performance of current state-of-art methods which are typically based on histograms over geometric properties. In this paper, we introduce 3DMatch1, a data-driven local feature learner that jointly learns a geometric feature representation and an associated metric function from a large collection of realworld scanning data. We represent 3D geometry using accumulated distance fields around key-point locations. This representation is suited to handle noisy and partial scanning data, and concurrently supports deep learning with convolutional neural networks directly in 3D. To train the networks, we propose a way to automatically generate correspondence labels for deep learning by leveraging existing RGB-D reconstruction algorithms. In our results, we demonstrate that we are able to outperform state-of-theart approaches by a significant margin. In addition, we show the robustness of our descriptor in a purely geometric sparse bundle adjustment pipeline for 3D reconstruction. 1All of our code, datasets, and trained models are publicly available: http://3dmatch.cs.princeton.edu/
A Hybrid Approach to Grapheme to Phoneme Conversion in Assamese
(2018)

Assamese is one of the low resource Indian languages. This paper implements both rule-based and data-driven grapheme to phoneme (G2P) conversion systems for Assamese. The rule-based system is used as the baseline which yields a word error rate of 35.3%. The data-driven systems are implemented using state-of-the-art sequence learning techniques such as —i) Joint-Sequence Model (JSM), ii) Recurrent Neural Networks with LTSM cell (LSTM-RNN) and iii) bidirectional LSTM (BiLSTM). The BiLSTM yields the lowest WER i.e., 18.7%, which is an absolute 16.6% improvement on the baseline system. We additionally implement the rules of syllabification for Assamese. The surface output is generated in two forms namely i) phonemic sequence with syllable boundaries, and ii) only phonemic sequence. The output of BiLSTM is fed as an input to Hybrid system. The Hybrid system syllabifies the input phonemic sequences to apply the vowel harmony rules. It also applies the rules of schwa-deletion as well as some rules in which the consonants change their form in clusters. The accuracy of the Hybrid system is 17.3% which is an absolute 1.4% improvement over the BiLSTM based G2P.
An innovative cost-effective smart meter with embedded non intrusive load monitoring
(2017)

Most of the time, domestic energy usage is invisible to the users. Households have only a vague idea about the amount of energy they are using for different purposes during the day, and the electric bill is usually the only monthly/bimonthly feedback. Making the energy consumption promptly visible can make the difference and foster a really efficient usage of the energy in a city. The emerging smart metering infrastructure has the potential to address some of these goals, by providing both households and electric utilities useful information about energy consumption, with a detailed breakdown to single appliance contribution. This paper presents a novel ultra-low-cost smart metering system capable of providing domestic AC load identification, by implementing both basic/typical smart meter measurement and a simple yet effective load disaggregation algorithm, which can detect and monitor domestic loads starting from a single point of measurement.
A Stable and Accurate Marker-Less Augmented Reality Registration Method
(2017)

Markerless Augmented Reality (AR) registration using the standard Homography matrix is unstable, and for image-based registration it has very low accuracy. In this paper, we present a new method to improve the stability and the accuracy of marker-less registration in AR. Based on the Visual Simultaneous Localization and Mapping (V-SLAM) framework, our method adds a three-dimensional dense cloud processing step to the state-of-the-art ORB-SLAM in order to deal with mainly the point cloud fusion and the object recognition. Our algorithm for the object recognition process acts as a stabilizer to improve the registration accuracy during the model to the scene transformation process. This has been achieved by integrating the Hough voting algorithm with the Iterative Closest Points(ICP) method. Our proposed AR framework also further increases the registration accuracy with the use of integrated camera poses on the registration of virtual objects. Our experiments show that the proposed method not only accelerates the speed of camera tracking with a standard SLAM system, but also effectively identifies objects and improves the stability of markerless augmented reality applications.
A Probabilistic Framework for Real-time 3D Segmentation using Spatial, Temporal, and Semantic Cues
(2016)

The best-matching segment is then assigned to this groundtruth bounding box for the evaluation metric described in our paper, as well as for the metric described below. Some previous works have evaluated 3D segmentation using the intersection-over-union metric on 3D points [5]. Note that our method segments the entire scene, as opposed to the method of Wang et al. [5], so the evaluation metric from Wang et al. [5] does not directly apply. However, we could modify the intersection-over-union metric [5] as follows: we can compute the fraction of ground-truth bounding boxes which have an intersection-over-union score less than a threshold τIOU , as
Mode-Seeking on Hypergraphs for Robust Geometric Model Fitting
(2015)

In this paper, we propose a novel geometric model fitting method, called Mode-Seeking on Hypergraphs (MSH), to deal with multi-structure data even in the presence of severe outliers. The proposed method formulates geometric model fitting as a mode seeking problem on a hypergraph in which vertices represent model hypotheses and hyperedges denote data points. MSH intuitively detects model instances by a simple and effective mode seeking algorithm. In addition to the mode seeking algorithm, MSH includes a similarity measure between vertices on the hypergraph and a "weight-aware sampling" technique. The proposed method not only alleviates sensitivity to the data distribution, but also is scalable to large scale problems. Experimental results further demonstrate that the proposed method has significant superiority over the state-of-the-art fitting methods on both synthetic data and real images.
Name Translation in Statistical Machine Translation - Learning When to Transliterate
(2008)

We present a method to transliterate names in the framework of end-to-end statistical machine translation. The system is trained to learn when to transliterate. For Arabic to English MT, we developed and trained a transliterator on a bitext of 7 million sentences and Google’s English terabyte ngrams and achieved better name translation accuracy than 3 out of 4 professional translators. The paper also includes a discussion of challenges in name translation evaluation.
Suppressing the azimuth ambiguities in synthetic aperture radar images
(1993)

This paper proposes a method for removing the azimuth ambiguities from synthetic aperture radar ( S A R ) images. The basic idea is to generate a two-dimensional reference function for S A R processing which provides, in addition to the matched filtering for the unaliased part of the received signal, the deconvolution of the azimuth ambiguities. This approach corresponds to an ideal filter concept, where an ideal impulse response function is obtained even in the presence of severe phase and amplitude errors. Modeling the sampled azimuth signal shows that the absolute phase value of the ambiguities cannot easily be determined due to their undersampling. The concept of the ideal filter is then extended to accommodate the undefined phase of the ambiguities and also the fading of the azimuth signal. Raw data from the E-SAR system have been used to verify the improvement in image quality by using the new method. It has a substantial significance in enabling the pulse-repetition frequency (PRF) constraints in the S A R system design to be relaxed and also for improving S A R image quality and interpretation.
Swara Histogram Based Structural Analysis And Identification Of Indian Classical Ragas
(2013)

This work is an attempt towards robust automated analysis of Indian classical ragas through machine learning and signal processing tools and techniques. Indian classical music has a definite heirarchical structure where macro level concepts like thaats and raga are defined in terms of micro entities like swaras and shrutis. Swaras or notes in Indian music are defined only in terms of their relation to one another (akin to the movable do-re-mi-fa system), and an inference must be made from patterns of sounds, rather than their absolute frequency structure. We have developed methods to perform scale-independent raga identification using a random forest classifier on swara histograms and achieved state-of-the-art results for the same. The approach is robust as it directly works on partly noisy raga recordings from Youtube videos without knowledge of the scale used, whereas previous work in this direction often use audios generated in a controlled environment with the desired scale. The current work demonstrates the approach for 8 ragas namely Darbari, Khamaj, Malhar, Sohini, Bahar, Basant, Bhairavi and Yaman and we have achieved an average identification accuracy of 94.28% through the framework.
Monitoring of Micro-sleep and Sleepiness for the Drivers Using EEG Signal
(2013)

................................................................................................................................................... 3 ACKNOWLEDGEMENT ................................................................................................................................. 5 LIST OF FIGURES .......................................................................................................................................... 8 LIST OF TABLES ............................................................................................................................................ 9 LIST OF ABBREVIATIONS ............................................................................................................................ 10 INTRODUCTION ......................................................................................................................................... 11 BACKGROUND ........................................................................................................................................... 13 METHODS .................................................................................................................................................. 17 PRE-PROCESSING ................................................................................................................................... 20 FEATURE EXTRACTION ........................................................................................................................... 24 SUPPORT VECTOR MACHINE ................................................................................................................. 31 RELATED WORK ......................................................................................................................................... 41 Extraction of Feature Information in EEG Signal ................................................................................... 41 Sleepiness and health problems in drivers who work driving some kind of vehicle with nocturnal and shift works ............................................................................................................................................. 43 SVM Regression Training with SMO ...................................................................................................... 48 EVALUATION AND CONCLUSION ............................................................................................................... 52 FUTURE WORK ........................................................................................................................................... 55 REFERENCES .............................................................................................................................................. 58
Improving Liver Lesion Detection using CT-to-PET FCN and GAN Networks
(2018)

We present a brief description of our recent work that was submitted for journal publication introducing a novel system for generation of virtual PET images using CT scans [1]. We combine a fully convolutional network (FCN) with a conditional generative adversarial network (GAN) to synthesize PET images from given input CT images. Clinically, such solutions may reduce the need for the more expensive and radioactive PET/CT scan. Quantitative evaluation was conducted using an existing lesion detection software, combining the synthesized PET as a false positive reduction layer for the detection of malignant lesions in the liver. Current results look promising showing a reduction in the average false positive per case while keeping the same true positive rate. The suggested solution is comprehensive and can be expanded to additional body organs, and different modalities.